We compare the momentum strategies to "naïve" uninformed strategies in Taiwan, Hong Kong, and Korea. The high participation of individual investors in these economies makes it an ideal setting to use the score function proposed by Banerjee and Hung (BH, 2011). As in BH we find that the average...

This paper proposes a Near Explosive Random-Coefficient autoregressive model for asset pricing which accommodates both the fundamental asset value and the recurrent presence of autonomous deviations or bubbles. Such a process can be stationary with or without fat tails, unit-root nonstationary...

This paper proposes a Near Explosive Random-Coefficient autoregressive model for asset pricing which accommodates both the fundamental asset value and the recurrent presence of autonomous deviations or bubbles. Such a process can be stationary with or without fat tails, unit-root nonstationary...

This paper examines whether tone (positive and negative) and volume of firm-specific news media content provide valuable information about future stock returns, using UK news media data from 1981-2010. The results indicate that both tone and volume of news media content significantly predict...

We create a market-wide measure of dispersion in options investors' expectations by aggregating across all stocks the dispersion in trading volume across moneynesses (DISP). DISP exhibits strong negative predictive power for future market returns and its information content is not subsumed by...

We compare actual R&D spend with the managerial rhetoric around technology and innovation contained within corporate disclosures of US-listed firms. We find that, whilst actual R&D spend and patents do not entice institutional investors to increase their stock holdings, firms that espouse...

This paper examines whether tone (positive and negative) and volume of firm-specific news media content provide valuable information about future stock returns, using UK news media data from 1981–2010. The results indicate that both tone and volume of news media content significantly predict...